Facial Recognition for Drunk People Using Thermal Imaging

Sancen-Plaza, Agustin; Contreras-Medina, Luis Miguel; Barranco-Gutiérrez, Alejandro I.; Villaseñor‐Mora, Carlos; Nolasco, Juan José Martínez; Padilla-Medina, José A.

doi:10.1155/2020/1024173

Cited by 13 publications

(5 citation statements)

References 26 publications

(31 reference statements)

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“…thermal imaging records the heat radiation from the face and body temperature even in a completely dark area, it has been suggested as an answer in the latest years [2][3][4]. The likelihood of creating a fake face pattern is greatly diminished since the temperature of the skin on the face is directly tied to the underlying blood vessels that are specific to every unique person [5].…”

Section: International Journal Of Electrical and Computer Engineering...mentioning

confidence: 99%

Human Face Emotions Recognition from Thermal Images Using DenseNet

Prasad

Chandana

2023

Int. j. electr. comput. eng. syst. (Online)

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In the current scenario face identification and recognition is an important technique in surveillance. The face is a necessary biometric in humans. Therefore face detection plays a major job in computer vision applications. Several face recognition and emotions classification approaches have been presented throughout the last few decades of research to improve the rate of face recognition for thermal pictures. However, in real-time, lighting conditions might change due to several factors, such as the different times of capture, weather, etc. Due to variations in lighting intensity, the performance of the facial expression recognition system is not good. This paper proposed a model for human thermal face detection and expression classification. Four main steps were involved in this research. Initially, the Difference of the Gaussian (DOG) filter is utilized to crop the input thermal images and then normalize the images using the median filter in pre-processing step. Then, Efficient Net is used for extracting features such as shape, location, and occurrences from thermal face images. After that, detect human faces utilized by the YOLOv4 technique to better emotions classification. Finally, classify the emotions on faces by using the DenseNet technique into seven emotions such as happy, sad, disgust, surprise, anger, fear, and neutral. The proposed method outperforms state-of-art techniques for face recognition on thermal pictures, and classifies the expressions, according to experimentations on the RGB-D-T database. The accuracy, precision, recall, and f1-score metrics will be utilized with the database to assess the efficacy of the proposed methodology. The proposed models achieve a high classification accuracy of 95.97% on the RGB-D-T database. Furthermore, the outcomes show good precision for various face recognition tasks.

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Section: International Journal Of Electrical and Computer Engineering...mentioning

confidence: 99%

Human Face Emotions Recognition from Thermal Images Using DenseNet

Prasad

Chandana

2023

Int. j. electr. comput. eng. syst. (Online)

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“…In the facial area, this biological behavior shows itself by increasing the temperature around the nose, forehead, and eye area [66]. Despite several challenges, recent research has shown an average percentage accuracy of %99.63 in identifying drunk individuals through thermal images [67].…”

Section: Facial Thermography Applicationsmentioning

confidence: 99%

A Novel Fully Annotated Thermal Infrared Face Dataset: Recorded in Various Environment Conditions and Distances From The Camera

Ashrafi¹,

Azarbayjania²,

Tabkhi³

2022

Preprint

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Facial thermography is one of the most popular research areas in infrared thermal imaging, with diverse applications in medical, surveillance, and environmental monitoring. However, in contrast to facial imagery in the visual spectrum, the lack of public datasets on facial thermal images is an obstacle to research improvement in this area. Thermal face imagery is still a relatively new research area to be evaluated and studied in different domains.The current thermal face datasets are limited in regards to the subjects' distance from the camera, the ambient temperature variation, and facial landmarks' localization. We address these gaps by presenting a new facial thermography dataset.This article makes two main contributions to the body of knowledge. First, it presents a comprehensive review and comparison of current public datasets in facial thermography. Second, it introduces and studies a novel public dataset on facial thermography, which we call it Charlotte-ThermalFace. Charlotte-ThermalFace contains more than10000 infrared thermal images in varying thermal conditions, several distances from the camera, and different head positions. The data is fully annotated with the facial landmarks, ambient temperature, relative humidity, the air speed of the room, distance to the camera, and subject thermal sensation at the time of capturing each image. Our dataset is the first publicly available thermal dataset annotated with the thermal sensation of each subject in different thermal conditions and one of the few datasets in raw 16-bit format. Finally, we present a preliminary analysis of the dataset to show the applicability and importance of the thermal conditions in facial thermography. The full dataset, including annotations, are freely available for research purpose at

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“…Sancen et al [ 16 ] presented a method for recognizing faces based on thermal pictures of inebriated faces. The Pontificia Universidad Católica de Valparaiso–Drunk Thermal Face database (PUCV-DTF) was utilized for the tests.…”

Section: Literature Surveymentioning

confidence: 99%

Objects and Action Detection of Human Faces through Thermal Images Using ANU-Net

Singothu

Chandana²

2022

Sensors

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Thermal cameras, as opposed to RBG cameras, work effectively in extremely low illumination situations and can record data outside of the human visual spectrum. For surveillance and security applications, thermal images have several benefits. However, due to the little visual information in thermal images and intrinsic similarity of facial heat maps, completing face identification tasks in the thermal realm is particularly difficult. It can be difficult to attempt identification across modalities, such as when trying to identify a face in thermal images using the ground truth database for the matching visible light domain or vice versa. We proposed a method for detecting objects and actions on thermal human face images, based on the classification of five different features (hat, glasses, rotation, normal, and hat with glasses) in this paper. This model is presented in five steps. To improve the results of feature extraction during the pre-processing step, initially, we resize the images and then convert them to grayscale level using a median filter. In addition, features are extracted from pre-processed images using principle component analysis (PCA). Furthermore, the horse herd optimization algorithm (HOA) is employed for feature selection. Then, to detect the human face in thermal images, the LeNet-5 method is used. It is utilized to detect objects and actions in face areas. Finally, we classify the objects and actions on faces using the ANU-Net approach with the Monarch butterfly optimization (MBO) algorithm to achieve higher classification accuracy. According to experiments using the Terravic Facial Infrared Database, the proposed method outperforms “state-of-the-art” methods for face recognition in thermal images. Additionally, the results for several facial recognition tasks demonstrate good precision.

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Facial Recognition for Drunk People Using Thermal Imaging

Cited by 13 publications

References 26 publications

Human Face Emotions Recognition from Thermal Images Using DenseNet

Human Face Emotions Recognition from Thermal Images Using DenseNet

A Novel Fully Annotated Thermal Infrared Face Dataset: Recorded in Various Environment Conditions and Distances From The Camera

Objects and Action Detection of Human Faces through Thermal Images Using ANU-Net

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